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Omniverse Cad To Simready

OpenAIOfficialCreated by OmniverseDistributed by OpenAI

  • Skill
  • Workflow
  • Software development
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

Coordinate the end-to-end CAD/source-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material/physics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references.
— the description by Omniverse

From the skill’s files

Taken from the skill’s own files (its description, section headings and stated usage). Not an AutoGineer summary, and not written by the author as one.

Coordinate the end-to-end CAD/source-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material/physics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references. Use this workflow skill when the user wants an end-to-end pipeline from a source asset to a SimReady asset or package. This skill coordinates existing conversion, authoring, validation, conformance, rendering, and packaging references directly. Do not replace the workflow with a single monolithic runner command.

What it does

  • When to Use
  • Prerequisites
  • Minimum Viable Scope
  • First Action
  • Instructions
  • Output Format

When to use it

  • Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material/physics assignment, SimReady conformance, validation, and…
  • Use this workflow skill when the user wants an end-to-end pipeline from a source asset to a SimReady asset or package.
  • This skill coordinates existing conversion, authoring, validation, conformance, rendering, and packaging references directly.
  • This skill is documentation-driven and does not ship scripts/run.py.
  • It should not depend on a repository checkout.
  • When a stage needs deterministic execution, run the portable script from that stage reference's installed directory.

Advantages

The skill’s files do not state this.

Usage examples

The skill’s files do not state this.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/openai/omniverse-cad-to-simready/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip omniverse-cad-to-simready.zip -d .agents/skills

Documentation by Omniverse

CAD to SimReady

When to Use

Use this workflow skill when the user wants an end-to-end pipeline from a source asset to a SimReady asset or package. This skill coordinates existing conversion, authoring, validation, conformance, rendering, and packaging references directly. Do not replace the workflow with a single monolithic runner command.

This skill is documentation-driven and does not ship scripts/run.py. It should not depend on a repository checkout. When a stage needs deterministic execution, run the portable script from that stage reference's installed directory. Shell is declared because this workflow invokes installed stage reference scripts directly; it still must not grow a monolithic runner.

Prerequisites

  • Prefer running the preflight reference first for deterministic setup. It installs or verifies local upstream checkouts, writes a cad-to-simready-preflight.json manifest, and exports PHYSICAL_AI_PREFLIGHT_MANIFEST plus PHYSICAL_AI_REQUIRE_PREFLIGHT=1 for downstream references.

  • Python 3.12 and uv (per repo README.md).

  • NVIDIA_API_KEY from https://build.nvidia.com when local Content Agents deployment will run. Already-running endpoints may instead use explicit endpoint variables plus usage tokens such as NGC_API_KEY, NVCF_API_KEY, or CONTENT_AGENTS_*_TOKEN.

  • Docker, NVIDIA Container Toolkit, and an NVIDIA GPU for Content Agents and OVRTX stages.

  • Local upstream checkouts under ${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams} when a downstream stage needs upstream scripts or specs.

Minimum Viable Scope

Conversion-only is a valid workflow request. When the user asks only to convert or smoke-test source asset conversion, set property_assignment_intent=skip, do not deploy Content Agents, run convert-to-usd, then run validate-usd-minimum on the generated USD if conversion succeeds.

Do not imply that uv sync installs every source converter runtime. URDF, MuJoCo/MJCF, and the repo Python dependencies are handled by the project environment, but NVIDIA-backed source conversion requires an installed and validated NVIDIA-Omniverse/usd-convert-cad checkout. If that runtime is missing or does not support the source, preserve the blocked conversion report and its install_hint instead of attempting an unrequested local build or substituting another converter.

First Action

For any broad CAD/source-asset to SimReady request, assume property_assignment_intent=run unless the user explicitly asks for conversion-only, validation-only, or no material/physics assignment.

Before invoking converter, validation, Content Agents, OVRTX, packaging, or FET helper scripts, run the preflight reference or verify an existing PHYSICAL_AI_PREFLIGHT_MANIFEST. Treat preflight as the mandatory dependency bootstrap step, not as workflow routing. If the user explicitly asks not to deploy services or asks for conversion-only/validation-only, use --skip-content-agents. When PHYSICAL_AI_REQUIRE_PREFLIGHT=1 is set and a required component is not ready in the manifest, downstream references must block with the preflight guardrail instead of rediscovering upstreams or services directly.

When property_assignment_intent=run, the first operational action after confirming the source path and resolving intent is to verify or deploy Content Agents services. Do this before asset-context inspection, converter dependency checks, conversion, validation, conformance, rendering, packaging, or upstream source builds.

Use healthy existing endpoints when available. If OVRTX, Material, or Physics endpoints are missing or unhealthy, run deploy-content-agents first and do not continue until the shared standalone OVRTX renderer plus independent Material and Physics service containers are healthy and exported through CONTENT_AGENTS_*_BASE_URL. Deploy the Texture Agent too when texture generation is requested.

If required deployment authentication is missing, ask the user for NVIDIA_API_KEY and wait. If a provided endpoint requires usage auth, ask for the appropriate usage token instead. If deployment cannot produce healthy services, report Content Agents readiness as blocked instead of proceeding to conversion.

Instructions

  1. Confirm the source asset path exists, resolve output_root, and classify the request as end-to-end, conversion-only, validation-only, or packaging.

  2. Resolve property_assignment_intent before running any asset inspection, converter probe, conversion, validation, conformance, rendering, or packaging step.

  3. Run preflight for the selected workflow targets, unless a ready PHYSICAL_AI_PREFLIGHT_MANIFEST is already configured. Source the generated env file before running downstream scripts. Treat preflight as dependency setup only: it may use a provided --source-asset, --source-format, or --conversion-tools value to scope dependency checks, but convert-to-usd and the upstream converter references still decide actual conversion support.

  4. Verify or deploy Content Agents services first when property_assignment_intent=run; block on missing authentication or unhealthy services instead of continuing.

  5. Read references/workflow.md and references/commands.md, then run only the stage references needed for the current request.

  6. Run identify-asset-context on the original source asset when web search is available or property assignment will run.

  7. Route the source through convert-to-usd, or skip conversion for existing USD input and treat the source path as the current USD path.

  8. Run validate-usd-minimum before expensive downstream work. Treat this as a viability gate only: record unit/profile issues such as metersPerUnit != 1.0, but do not run simready-conform-profile, FET001, or any other FET repair before Content Agents assignment when property assignment will run.

  9. Run Content Agents material, physics, and optional texture assignment on the converted/minimum-valid USD when requested or required.

  10. Run simready-conform-profile on the latest simulation USD path after property assignment and preserve every selected FET repair report.

  11. Run validation gates in order: omni-asset-validate, omni-asset-validate-geometry, omni-asset-validate-physics, and simready-validate.

  12. Rerun simready-conform-profile when simready-validate reports a repairable requirement, then rerun profile validation on the newest authored USD.

  13. Run ovrtx-render-service when preview, thumbnail, or inspection images are requested. When package outputs are requested, run assemble-package-source next to create the clean deliverable/ package source from the final USD and thumbnail, then run nv-core-package-sample and nv-core-package-sample-validation on that deliverable folder only.

  14. Emit the consolidated workflow report with the final USD path, all stage reports, validation findings, rerun reasons, and next work.

Use the simready-conform-profile reference only after property assignment when property_assignment_intent=run. It routes feature repair to upstream SimReady Foundation FET skills such as simready-foundation-conform-fet-000-core, simready-foundation-conform-fet-001-minimal, simready-foundation-conform-fet-004-simulate-multi-body-physics, and simready-foundation-conform-fet-005-simulate-grasp-physics from branch main.

If simready-validate reports a repairable requirement after the first conformance pass, feed the structured requirement IDs back into the simready-conform-profile reference before writing the final result. In particular, GSP.001 is owned by upstream simready-foundation-conform-fet-005-simulate-grasp-physics; run that skill when a vision-capable agent can inspect visual evidence or explicit grasp points were provided, otherwise record the FET005 step as blocked by missing vision/points instead of treating it as an optional preview task. For RB.MB.001, route the failure to upstream simready-foundation-conform-fet-004-simulate-multi-body-physics. Do not assume multiple visual prims are multiple rigid bodies; inspect UsdPhysics.RigidBodyAPI applications. When the Physics Agent report shows composed topology optimization or the USD has existing component colliders/part roots and the profile validator reports FET004/RB.MB.001, FET004 should promote those existing components into rigid bodies without creating geometry. Do not mark the gate not applicable until after confirming there are fewer than two reusable body candidates.

Output Format

Emit a consolidated workflow report in Markdown, and include JSON when the workflow writes structured artifacts. The report must include:

  • Overall status: passed, blocked, failed, or needs_rerun.

  • Request summary: source asset path, detected source format, output root, selected SimReady profile/version, and property assignment intent.

  • Ordered stage results: stage reference, input artifact, output USD or USDZ path, report path, status, blocker reason, and rerun reason when applicable.

  • Content Agents readiness and property assignment results with service URLs, tokens, and credentials redacted.

  • Conformance and validation findings grouped by gate, requirement ID, selected FET repair reference, repair-loop attempt, and final disposition.

  • Final artifacts: final reported USD path, render preview path when requested, package root and package validation report when packaging ran, Markdown report path, JSON report path when present, and recommended next work.

Detailed References

Read only the references needed for the current request:

  • references/preflight/README.md: deterministic local setup, manifest/env contract, Linux and Windows wrappers, Content Agents deployment opt-out, and guardrail behavior.

  • references/workflow.md: inputs, source routing, detailed workflow, validation policy, output report fields, approval points, and next steps.

  • references/commands.md: concrete portable script command patterns for each stage.

  • references/assemble-package-source/README.md: two-zone package source assembly, canonical root USD naming, thumbnail placement, and self-contained deliverable checks.

Publishing Layout Notes

Use skills/omniverse-cad-to-simready/ as the source of truth for this product repo's skill. The .agents/skills symlink is a compatibility alias for local agentskills.io-style discovery, and .codex/skills and .claude/skills are agent-specific compatibility aliases.

Frontmatter keeps version and tools at top level for agentskills.io runtime compatibility. NVCARPS discoverability fields live under metadata.

The nested references/ tree is intentional. It keeps one public catalog skill while retaining script-bearing atomic stage references, upstream handoff notes, and router documentation under the workflow. Do not flatten those references or promote nested README references to sibling SKILL.md files unless the repo's publishing model changes.

Limitations

  • This workflow coordinates existing conversion, property assignment, conformance, validation, rendering, and packaging skills; it does not replace them with a single monolithic runner command.

  • Stop at the first failing deployment, conversion, property-assignment, or conformance authoring gate unless the user explicitly asks for best-effort continuation.

  • Upstream simready-foundation-conform-fet-005-simulate-grasp-physics needs visual review or explicit grasp points before it can author a meaningful grasp vector.

Troubleshooting

SymptomCauseFix
Downstream reference reports that cad-to-simready preflight has not prepared a componentPHYSICAL_AI_REQUIRE_PREFLIGHT=1 is set, but the manifest is missing or the required runtime/service is not readyRun preflight/scripts/preflight.py, source the generated env file, or explicitly disable service deployment with --skip-content-agents only when Content Agents are out of scope.
Workflow stops on GSP.001 and reports the failure as unclassifiedVisual evidence or explicit grasp points were not provided to FET005Run upstream simready-foundation-conform-fet-005-simulate-grasp-physics only after a vision-capable agent has reviewed the asset, or pass explicit grasp points. Otherwise report the FET005 step as blocked, not failed.
Validation fails after a meaningful USD artifact already existsWorkflow stopped at the first validation findingContinue remaining diagnostic gates and mark the result needs_rerun. Do not stop at validation findings once a USD artifact has been produced.
Property-assignment stage fails with a missing service endpointContent Agents service was not deployed before conversionRun deploy-content-agents first. Do not start asset inspection, conversion, validation, conformance, rendering, or packaging before Content Agents readiness when property assignment will run.
Material Agent reports that rendering produced 0 images after unit or profile repairA FET repair, commonly FET001 unit normalization, was applied before Material Agent and changed the USD layering/scene state consumed by the serviceRerun assignment from the converted/minimum-valid USD: Material Agent first, then Physics Agent, then run simready-conform-profile and FET repairs on the latest service-authored USD.
Material or Physics Agent local optimized path reports Permission denied: '/app/.build-resources/scene_optimizer_core/python'Local Docker Scene Optimizer bundle permissions prevent the non-root service user from reading the packaged SO runtimeRepair the relevant local container with docker exec --user root content-material-agent-service chmod -R a+rX /app/.build-resources/scene_optimizer_core or docker exec --user root content-physics-agent-service chmod -R a+rX /app/.build-resources/scene_optimizer_core, then rerun the same optimized agent command. Do not treat the no-optimizer fallback as the root cause for instanced/prototype assets.
RB.MB.001 fails even though the asset has many primsThe profile counts UsdPhysics.RigidBodyAPI prims, not visual or collider prims; Physics Agent may author one root rigid bodyRoute to upstream simready-foundation-conform-fet-004-simulate-multi-body-physics. First ensure Physics Agent used composed-topology optimization when applicable, then promote existing component colliders/part roots when the active profile reports FET004/RB.MB.001 and no geometry must be invented.

Hard Rules

  • Prefer the preflight manifest for local upstream roots, converter executables, SimReady validation runtime, OVRTX endpoint, and Content Agents service URLs. When PHYSICAL_AI_REQUIRE_PREFLIGHT=1 is set, do not bypass the manifest with direct upstream discovery.

  • Do not run asset inspection, converter probes, local upstream builds, conversion, validation, conformance, rendering, or packaging before Content Agents readiness when property assignment will run.

  • Use stage-specific installed reference scripts directly. Do not add or call a single omniverse-cad-to-simready runner command.

  • For source conversion, delegate to the convert-to-usd reference; do not substitute another converter for CAD or mesh formats.

  • For property assignment, use Content Agents references as separate atomic steps: material first, then physics, then texture only when requested.

  • When property assignment will run, do not run simready-conform-profile or any FET helper before Content Agents. Validate minimum USD first, then run Content Agents on that converted/minimum-valid USD, then apply FET repairs to the latest service-authored USD.

  • When property assignment will run, do not run simready-validate or any SimReady profile validation before Content Agents. The only validation gate allowed before service calls is validate-usd-minimum, which is a basic USD viability check.

  • Stop at the first failing deployment, conversion, property-assignment, or conformance authoring gate unless the user explicitly asks for best-effort continuation.

  • Do not stop at validation findings after a meaningful USD artifact exists. Continue remaining diagnostic gates and mark the result needs_rerun.

  • Do not leave a GSP.001 profile failure as an unclassified final finding. Route it to upstream simready-foundation-conform-fet-005-simulate-grasp-physics; if the current agent cannot inspect renders or no explicit grasp points are available, report a blocked FET005 repair with the visual evidence path or missing input reason.

  • Preserve every stage report and pass the concrete output USD path from each report into the next stage.

Files

references/deploy-content-agents/README.md

Deploy Content Agents

When to Use

Use this reference when the user asks to deploy, start, configure, or troubleshoot NVIDIA Omniverse Content Agents services. This repo does not ship a deployment runner or duplicate service runbooks; it selects the target, resolves the upstream checkout, enforces repo-local readiness policy, and hands off to the upstream deployment skills.

This reference is documentation-driven, does not ship scripts/run.py, and should not depend on this repository checkout.

Prerequisites

  • NVIDIA_API_KEY from https://build.nvidia.com for provider-backed services.

  • Docker, Docker Compose v2, NVIDIA Container Toolkit, an NVIDIA driver, and an NVIDIA GPU on the deployment host.

  • A normalized upstream checkout of https://github.com/nvidia-omniverse/content-agents on branch main.

Upstream Reference

Use the NVIDIA Omniverse Content Agents main deployment skills as the source of truth:

TargetUpstream skill
Material Agenthttps://github.com/nvidia-omniverse/content-agents/blob/main/.codex/skills/deploy-material-agent-docker/SKILL.md
Physics Agenthttps://github.com/nvidia-omniverse/content-agents/blob/main/.codex/skills/deploy-physics-agent-docker/SKILL.md
Texture Agenthttps://github.com/nvidia-omniverse/content-agents/blob/main/.codex/skills/deploy-texture-agent-docker/SKILL.md
OVRTX rendererhttps://github.com/nvidia-omniverse/content-agents/blob/main/.codex/skills/deploy-ovrtx-docker/SKILL.md

Repository: https://github.com/nvidia-omniverse/content-agents, branch main

If browser or raw-file fetches are blocked, use a local clone checked out to main and read .codex/skills/<skill-name>/SKILL.md from that checkout. Resolve it from CONTENT_AGENTS_UPSTREAM_ROOT, then $PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT/content-agents, then $HOME/.physical-ai-skill-hub/upstreams/content-agents. Do not scan broad developer workspaces.

Do not duplicate full upstream deployment runbooks here.

Inputs

Collect:

InputRequirement
target_servicematerial, physics, texture, or ovrtx.
content_agents_rootOptional explicit upstream checkout path.
deployment_modeLocal container deployment or reuse of healthy existing services.
render_endpointRequired for render-dependent agent services.
env_keysProvider/API keys required by the upstream skill. Never print or commit secrets.

Instructions

  1. Resolve the upstream checkout from the normalized root policy.

  2. Read the matching upstream deployment skill before issuing service-specific commands.

  3. Confirm required secrets are available from local shell state or a private .env; if missing, ask the user and wait.

  4. For material, physics, or texture, deploy or reuse OVRTX first when the selected upstream skill requires rendering.

  5. Prefer a shared standalone OVRTX renderer plus independently deployed agent services for this workflow. Use bundled agent-specific renderer stacks only when the user asks for isolation or the upstream skill requires it.

  6. Preserve upstream secret handling, build steps, image names, health checks, and service-specific environment details.

  7. Verify the renderer and selected agent health endpoints before exporting CONTENT_AGENTS_*_BASE_URL or RENDER_ENDPOINT.

  8. Return to the caller only after the requested service is healthy, or report the missing prerequisite as blocked.

Headless / Nested Host Notes

Use this section only as deployment-readiness guidance for nested or headless hosts. The upstream content-agents skills still own the actual Docker Compose files, commands, image names, ports inside containers, and service-specific environment variables.

Single GPU + Cloud VLM

For local evaluation, prefer one shared standalone OVRTX renderer on the local GPU plus independent Material, Physics, and Texture Agent service containers that use NVIDIA_API_KEY from https://build.nvidia.com for provider-backed VLM calls. This topology does not require a separate local VLM GPU. Verify the OVRTX host endpoint first, then verify each agent service reports configured API keys before exporting CONTENT_AGENTS_*_BASE_URL.

  • Confirm Docker Compose v2 is installed before following the upstream deployment skills; the legacy docker-compose binary is not sufficient when upstream expects docker compose.

  • In nested Docker environments, overlay storage can fail with invalid argument. If Docker cannot start containers, check whether the host needs a vfs storage-driver fallback before retrying the upstream deployment.

  • Avoid changing Docker daemon settings in a shared or user-provided SSH session until the user approves that risk; prefer a fresh disposable session for deployment experiments.

  • Treat the Xvfb display number as a configurable deployment input. If OVRTX exits during startup and the logs show a display conflict, choose an unused display such as :100 instead of assuming the default display is free.

  • Prefer distinct host endpoints for the shared renderer and each independently deployed agent service. A proven local layout is OVRTX on 8001, Material Agent on 8100, Physics Agent on 8200, and Texture Agent on 8300 when texture generation is needed; export the corresponding host URLs only after those endpoints respond healthy.

  • A container-internal OVRTX healthcheck can be a false negative when the externally mapped host /health endpoint is healthy. Record both the container health result and the host endpoint result before declaring the renderer blocked.

Handoff Map

TargetAfter deployment
Material AgentSet CONTENT_AGENTS_MATERIAL_AGENT_BASE_URL, then use material-agent-client.
Physics AgentSet CONTENT_AGENTS_PHYSICS_AGENT_BASE_URL, then use physics-agent-client.
Texture AgentSet CONTENT_AGENTS_TEXTURE_AGENT_BASE_URL, then use texture-agent-client.
OVRTXSet RENDER_ENDPOINT or OVRTX_RENDER_ENDPOINT for render clients.

Limitations

  • This reference selects targets and readiness gates; upstream deployment skills own commands and service internals.

  • It does not call already-running services for asset enrichment.

  • It does not publish Docker Compose, docker run, port, image-name, or in-container environment recipes. Read those from upstream.

Troubleshooting

SymptomAction
Upstream skill URL cannot be fetchedUse the local content-agents clone checked out to main.
Required API key is missingAsk the user for NVIDIA_API_KEY for deployment and wait. Usage tokens for already-running endpoints belong to the client references, not this deployment reference.
Service health is not readyFollow the selected upstream deployment skill's health-check section.
Renderer-dependent agent cannot reach OVRTXUse the upstream renderer and agent deployment skills together; do not patch this repo with local Docker recipes.
Nested host cannot start Docker containersCheck Docker Compose v2, GPU visibility, NVIDIA Container Toolkit, and whether the host requires the vfs storage-driver fallback.
OVRTX exits in a headless sessionCheck Xvfb logs and retry the upstream deployment with an unused display value instead of assuming :99 is available.
Container health is unhealthy but the mapped host endpoint respondsTreat this as a healthcheck mismatch until the upstream deployment skill confirms the container-internal and host-mapped ports. Use the host endpoint only after /health reports renderer readiness.

Pass/Fail Policy

Report blocked rather than guessing when:

  • the upstream checkout is inaccessible

  • Docker/GPU/container prerequisites fail

  • required credentials are missing

  • the selected upstream deployment skill does not support the requested mode

  • health checks do not pass

Next Steps

After a service is healthy, return to content-agents or the selected service wrapper reference for the asset workflow.

Requirements and compatibility

Written for: OpenAI Codex

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Python

The author says: Orchestrator skill. Managed Content Agents deployment requires NVIDIA_API_KEY (build.nvidia.com), Docker + NVIDIA Container Toolkit + GPU, Python 3.12, and an upstream checkout of nvidia-omniverse/content-agents on branch main. Reused/provided endpoints may instead use explicit endpoint and usage-token environment variables. Linux/macOS only.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network
  • Downloads piped into a shell
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Omniverse
Publisher
OpenAI (Official)
Distributed by
OpenAI
Source
openai/plugins · plugins/nvidia/skills/omniverse-cad-to-simready · commit d420d13
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (written by AutoGineer)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

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